{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0,metadata =pyreadstat.read_sav(R\"C:\\Users\\PC\\Desktop\\stasistic-2022s\\企业形象调查原始数据.sav\",apply_value_formats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1,metadata = mytools.read_spss(R\"C:\\Users\\PC\\Desktop\\stasistic-2022s\\企业形象调查原始数据.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df1[df1.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.dropna(thresh=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "                 0\n",
       "问卷编号       float64\n",
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       "伙食费       category\n",
       "常去的快餐店    category\n",
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       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
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       "快速便捷       float64\n",
       "价格昂贵       float64\n",
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                 0\n",
       "问卷编号         int32\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.astype({'问卷编号':'int'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "一般     224\n",
       "不错     139\n",
       "很好      33\n",
       "不好      17\n",
       "0.0      3\n",
       "Name: 服务态度, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['服务态度'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in df4.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性      211\n",
      "女性      204\n",
      "22.0      1\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     138\n",
      "九百到一千二    120\n",
      "一千二以上      85\n",
      "三百到六百      73\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       136\n",
      "德克士        78\n",
      "从不去快餐店     77\n",
      "麦当劳        62\n",
      "必胜客        44\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    188\n",
      "时尚新颖     91\n",
      "中西合璧     88\n",
      "古板单调     32\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般     224\n",
      "不错     139\n",
      "很好      33\n",
      "不好      17\n",
      "0.0      3\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    123\n",
      "只知道名字      120\n",
      "只知道几项       99\n",
      "大致了解        65\n",
      "非常了解         7\n",
      "0.0          2\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     161\n",
      "从未看过     113\n",
      "有时会看到     91\n",
      "常常会看到     51\n",
      "0.0        0\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            167\n",
      "到该公司也不错        118\n",
      "不知道             90\n",
      "一定想办法到该公司工作     40\n",
      "0.0              1\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      154\n",
      "不知道      111\n",
      "买了也不错    100\n",
      "一定会买      50\n",
      "0.0        1\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     166\n",
      "一流      84\n",
      "三流      84\n",
      "不知道     81\n",
      "0.0      1\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道几项      129\n",
      "只知道名字      128\n",
      "大致了解       104\n",
      "一点印象都没有     33\n",
      "非常了解        18\n",
      "0.0          4\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "常常会看到    118\n",
      "有时会看到    112\n",
      "从未看过      45\n",
      "0.0        1\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        143\n",
      "不想去            141\n",
      "不知道             85\n",
      "一定想办法到该公司工作     44\n",
      "0.0              3\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    140\n",
      "不想买      117\n",
      "不知道      102\n",
      "一定会买      54\n",
      "0.0        3\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     198\n",
      "一流     131\n",
      "不知道     52\n",
      "三流      32\n",
      "0.0      3\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      150\n",
      "只知道名字      128\n",
      "大致了解        89\n",
      "一点印象都没有     38\n",
      "非常了解         6\n",
      "0.0          5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     142\n",
      "有时会看到    127\n",
      "常常会看到     99\n",
      "从未看过      43\n",
      "0.0        4\n",
      "5.0        1\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            147\n",
      "到该公司也不错        145\n",
      "不知道             87\n",
      "一定想办法到该公司工作     33\n",
      "0.0              4\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      135\n",
      "不知道      121\n",
      "买了也不错    120\n",
      "一定会买      36\n",
      "0.0        4\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     170\n",
      "一流     126\n",
      "不知道     79\n",
      "三流      38\n",
      "0.0      3\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      148\n",
      "只知道几项      106\n",
      "一点印象都没有     80\n",
      "大致了解        68\n",
      "非常了解        12\n",
      "0.0          2\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     140\n",
      "有时会看到    118\n",
      "从未看过      87\n",
      "常常会看到     70\n",
      "0.0        1\n",
      "5.0        0\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            161\n",
      "不知道            124\n",
      "到该公司也不错         97\n",
      "一定想办法到该公司工作     29\n",
      "0.0              5\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      149\n",
      "不想买      130\n",
      "买了也不错     95\n",
      "一定会买      38\n",
      "0.0        4\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     150\n",
      "一流     114\n",
      "不知道     90\n",
      "三流      58\n",
      "0.0      4\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('性别==22')\n",
    "df5.loc[322,'性别'] = '男性'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('必胜客综合评价==0.0')\n",
    "df5.loc[352,'必胜客综合评价'] = '不知道'\n",
    "df5.loc[353,'必胜客综合评价'] = '不知道'\n",
    "df5.loc[357,'必胜客综合评价'] = '不知道'\n",
    "df5.loc[222,'必胜客综合评价'] = '不知道'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       416.000000\n",
       "mean        513.247596\n",
       "std        4893.048164\n",
       "min           1.000000\n",
       "25%         140.750000\n",
       "50%         259.000000\n",
       "75%         392.000000\n",
       "max      100000.000000\n",
       "Name: 问卷编号, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['问卷编号'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       416.000000\n",
      "mean        513.247596\n",
      "std        4893.048164\n",
      "min           1.000000\n",
      "25%         140.750000\n",
      "50%         259.000000\n",
      "75%         392.000000\n",
      "max      100000.000000\n",
      "Name: 问卷编号, dtype: float64\n",
      "count    416.000000\n",
      "mean      24.127404\n",
      "std        6.501946\n",
      "min       10.000000\n",
      "25%       20.000000\n",
      "50%       22.000000\n",
      "75%       26.000000\n",
      "max       60.000000\n",
      "Name: 年龄, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.281250\n",
      "std        0.450151\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 欢乐聚餐, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.137019\n",
      "std        0.344282\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 快乐童年, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.293269\n",
      "std        0.455809\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 都市生活, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.170673\n",
      "std        0.598959\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max       10.000000\n",
      "Name: 洋溢青春, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.429669\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        4.000000\n",
      "Name: 其他感受, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.151442\n",
      "std        0.358911\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 健康安全, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.572115\n",
      "std        0.495368\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速便捷, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.314904\n",
      "std        0.465037\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 价格昂贵, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.165865\n",
      "std        0.372408\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 口感不好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.163462\n",
      "std        0.370231\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 品类太少, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.076923\n",
      "std        0.266790\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他评价, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.456731\n",
      "std        0.498724\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速好吃, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.192308\n",
      "std        0.394588\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 服务好, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 种类多, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.168269\n",
      "std        0.374556\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 价格便宜, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.086538\n",
      "std        0.281496\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他吸引因素, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.319712\n",
      "std        0.466926\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 服务态度需改进, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 送餐速度, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.358173\n",
      "std        0.480041\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 就餐环境, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.391827\n",
      "std        0.488746\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品价格, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.237981\n",
      "std        0.426360\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 促销活动, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.295673\n",
      "std        0.456894\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 产品种类, dtype: float64\n",
      "count    416.000000\n",
      "mean       0.045673\n",
      "std        0.209026\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他改进因素, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = '常去的快餐店==\"麦当劳\"'\n",
    "temp = df5.query(c1)[['常去的快餐店']]\n",
    "df6 = df5.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df6.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = df['德克士类型感知'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>德克士类型感知</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>普遍大众</td>\n",
       "      <td>158</td>\n",
       "      <td>44.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中西合璧</td>\n",
       "      <td>81</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>时尚新颖</td>\n",
       "      <td>74</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>古板单调</td>\n",
       "      <td>25</td>\n",
       "      <td>7.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>其他类型</td>\n",
       "      <td>16</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>总和</td>\n",
       "      <td>354</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  德克士类型感知   个数    百分比\n",
       "0    普遍大众  158  44.63\n",
       "1    中西合璧   81  22.88\n",
       "2    时尚新颖   74   20.9\n",
       "3    古板单调   25   7.06\n",
       "4    其他类型   16   4.52\n",
       "5      总和  354  99.99"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['德克士类型感知'] = df['德克士类型感知'].value_counts().index\n",
    "b['个数'] = df['德克士类型感知'].value_counts().values\n",
    "b['百分比'] = df['德克士类型感知'].value_counts(normalize=True).values * 100\n",
    "b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'德克士类型感知':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "pd.concat([b,total_row],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>德克士类型感知</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>普遍大众</td>\n",
       "      <td>158</td>\n",
       "      <td>44.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中西合璧</td>\n",
       "      <td>81</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>时尚新颖</td>\n",
       "      <td>74</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>古板单调</td>\n",
       "      <td>25</td>\n",
       "      <td>7.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>其他类型</td>\n",
       "      <td>16</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>总和</td>\n",
       "      <td>354</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  德克士类型感知   个数    百分比\n",
       "0    普遍大众  158  44.63\n",
       "1    中西合璧   81  22.88\n",
       "2    时尚新颖   74   20.9\n",
       "3    古板单调   25   7.06\n",
       "4    其他类型   16   4.52\n",
       "5      总和  354  99.99"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'德克士类型感知')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>136</td>\n",
       "      <td>38.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德克士</td>\n",
       "      <td>78</td>\n",
       "      <td>22.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>77</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>44</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>其他</td>\n",
       "      <td>19</td>\n",
       "      <td>5.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>354</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  136  38.42\n",
       "1     德克士   78  22.03\n",
       "2  从不去快餐店   77  21.75\n",
       "3     必胜客   44  12.43\n",
       "4      其他   19   5.37\n",
       "5     麦当劳    0    0.0\n",
       "6      总和  354  100.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'德克士类型感知')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>伙食费</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比（%）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>三百到六百</td>\n",
       "      <td>65</td>\n",
       "      <td>18.36</td>\n",
       "      <td>18.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>六百到九百</td>\n",
       "      <td>120</td>\n",
       "      <td>33.9</td>\n",
       "      <td>52.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>九百到一千二</td>\n",
       "      <td>97</td>\n",
       "      <td>27.4</td>\n",
       "      <td>79.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>一千二以上</td>\n",
       "      <td>72</td>\n",
       "      <td>20.34</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>354</td>\n",
       "      <td>100.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      伙食费   个数    百分比 累计百分比（%）\n",
       "0   三百到六百   65  18.36    18.36\n",
       "1   六百到九百  120   33.9    52.26\n",
       "2  九百到一千二   97   27.4    79.66\n",
       "3   一千二以上   72  20.34    100.0\n",
       "4      总和  354  100.0         "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import CategoricalDtype\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['三百到六百','六百到九百','九百到一千二', '一千二以上'], ordered=True)\n",
    "df = df.astype({'伙食费':cat_dtype})\n",
    "mytools.ordinal_desc(df,'伙食费')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'伙食费',sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    354.000000\n",
       "mean       5.398305\n",
       "std        2.124216\n",
       "min        1.000000\n",
       "25%        4.000000\n",
       "50%        6.000000\n",
       "75%        7.000000\n",
       "max        9.000000\n",
       "Name: 性别经济维度, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['性别经济维度']= df['性别'].cat.codes + df['伙食费'].cat.codes + df['常去的快餐店'].cat.codes\n",
    "df['性别经济维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['德克士类型感知'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>德克士类型感知</th>\n",
       "      <th>中西合璧</th>\n",
       "      <th>其他类型</th>\n",
       "      <th>古板单调</th>\n",
       "      <th>时尚新颖</th>\n",
       "      <th>普遍大众</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三百到六百</th>\n",
       "      <td>19.75</td>\n",
       "      <td>31.25</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.86</td>\n",
       "      <td>19.62</td>\n",
       "      <td>18.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六百到九百</th>\n",
       "      <td>33.33</td>\n",
       "      <td>12.50</td>\n",
       "      <td>48.0</td>\n",
       "      <td>28.38</td>\n",
       "      <td>36.71</td>\n",
       "      <td>33.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九百到一千二</th>\n",
       "      <td>32.10</td>\n",
       "      <td>37.50</td>\n",
       "      <td>24.0</td>\n",
       "      <td>35.14</td>\n",
       "      <td>20.89</td>\n",
       "      <td>27.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一千二以上</th>\n",
       "      <td>14.81</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.62</td>\n",
       "      <td>22.78</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "德克士类型感知   中西合璧   其他类型  古板单调   时尚新颖   普遍大众     合计\n",
       "伙食费                                             \n",
       "三百到六百    19.75  31.25   8.0  14.86  19.62  18.36\n",
       "六百到九百    33.33  12.50  48.0  28.38  36.71  33.90\n",
       "九百到一千二   32.10  37.50  24.0  35.14  20.89  27.40\n",
       "一千二以上    14.81  18.75  20.0  21.62  22.78  20.34"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为：0.016，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_y = mytools.goodmanKruska_tau_y(df, '德克士类型感知', '伙食费')\n",
    "f'tau_y值为：{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>性别</th>\n",
       "      <th>女性</th>\n",
       "      <th>男性</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>三百到六百</th>\n",
       "      <td>23.03</td>\n",
       "      <td>13.64</td>\n",
       "      <td>18.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六百到九百</th>\n",
       "      <td>33.71</td>\n",
       "      <td>34.09</td>\n",
       "      <td>33.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九百到一千二</th>\n",
       "      <td>24.72</td>\n",
       "      <td>30.11</td>\n",
       "      <td>27.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一千二以上</th>\n",
       "      <td>18.54</td>\n",
       "      <td>22.16</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "性别         女性     男性     合计\n",
       "伙食费                        \n",
       "三百到六百   23.03  13.64  18.36\n",
       "六百到九百   33.71  34.09  33.90\n",
       "九百到一千二  24.72  30.11  27.40\n",
       "一千二以上   18.54  22.16  20.34"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['三百到六百','六百到九百', '九百到一千二','一千二以上' ], ordered=True)\n",
    "df = df.astype({'伙食费':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['性别'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12164836567926456"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "x = df['性别'].cat.codes\n",
    "y = df['伙食费'].cat.codes\n",
    "dy = stats.somersd(x, y)\n",
    "dy.statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'萨莫司dy值为：0.122，该值属于极弱相关或不相关，p值为0.038，拒绝虚无假设，接收研究假设。'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = dy.pvalue\n",
    "f\"萨莫司dy值为：{dy.statistic:.3f}，该值属于{mytools.draw_on_corr(dy.statistic)}，p值为{p:.3f}，{mytools.p_result(p)['conclusion']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['感知评价维度']= df['德克士类型感知'].cat.codes + df['必胜客综合评价'].cat.codes + df['常去的快餐店'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.gen_scatter(df,'性别经济维度','感知评价维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'积矩相关系数r为：0.551，决定系数r平方为：0.303，相关强度为中等。'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r, p = stats.pearsonr(df['感知评价维度'], df['性别经济维度'])\n",
    "f\"积矩相关系数r为：{r:.3f}，决定系数r平方为：{r*r:.3f}，相关强度为{mytools.draw_on_r(r*r)}。\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytools.to_sav(df_clean,metadata,'./data/indentity问卷数据数据清理后.sav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e593ac106456af50ce7af38f9671c411b49d6cd90f9b885e167f0f594e09038c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
